One of the most persistent bad ideas in finance is the belief that if we just “work harder” at the math, we can make economics as predictive as physics. Build better models, fit more data, solve the markets. It’s a comforting fantasy, especially for quants.

It’s also wrong.

Andrew Lo and Mark Mueller’s paper, “Physics Envy May Be Hazardous to Your Wealth,” explains why: financial markets aren’t governed by natural laws. They’re governed by human behavior — and humans are messy, inconsistent, and react to incentives. The more we pretend markets behave like idealized mechanical systems, the more likely our models are to fail when it matters most.

At the heart of their argument is a taxonomy of uncertainty:

• Level 1: Complete Certainty
  • Think Newtonian physics. You know the exact state of a system at all times if you know the starting conditions. Spoiler: markets aren’t like this.

• Level 2: Risk without Uncertainty
  • You know all the possible outcomes and their probabilities (think roulette). This is the assumption underlying most financial models. It’s also not reality.

• Level 3: Fully Reducible Uncertainty
  • You don’t know the exact odds, but with enough data, you could estimate them. Some parts of finance look like this in stable periods.

• Level 4: Partially Reducible Uncertainty
  • The rules change. Regimes shift. Parameters aren’t stable. You can guess, but you’ll never pin it down completely. This is where most real markets live.

• Level 5: Irreducible Uncertainty
  • Black swans, system breaks, things you didn’t even know you needed to model. Good luck.

Once you move past Level 2, standard models start to leak—and pretending otherwise is why financial crises keep looking like “surprises” in hindsight.

The authors offer two simple but powerful examples:

1. A harmonic oscillator (basic physics)
   Add some noise, change regimes, and suddenly even simple systems become harder to predict. Real-world markets are already noisier and less stable than this toy model.
2. A market-neutral stat-arb strategy
   A classic mean-reversion strategy looks great under Level 2 assumptions. But introduce non-stationarity, regime shifts, or liquidity constraints, and the edge evaporates. Worse, you won’t always realize when it happens.

In both cases, the problem isn’t that modeling is useless — it’s that models often operate at the wrong level of uncertainty. Treat a Level 4 problem like it is Level 2, and you get blindsided.

They also tackle a few myths head-on:

• The quants didn’t cause the 2008 crisis by themselves.
  • Bad incentives, regulatory failures, and overreliance on false precision all contributed. It wasn’t that formulas are evil — it’s that the wrong formulas were trusted too much by people who didn’t understand their limits.
• Finance isn’t doomed to be unscientific forever.
  • But it needs to be more like biology than physics: messy, evolutionary, adaptive. Finance will never have “laws” like thermodynamics. It will have heuristics that work until they don’t.
• A good model isn’t “true.”
  • It’s useful. It manages risk under known uncertainty and adapts when conditions change. Thinking otherwise is malpractice.

They even propose an “uncertainty checklist”: a pragmatic way to remind yourself what level of uncertainty your strategy, your model, or your investment decision actually lives in.

The market doesn’t reward cleverness. It rewards adaptability.

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